Knowledge Distillation-driven Communication Framework for Neural Networks: Enabling Efficient Student-Teacher Interactions
This paper presents a novel framework for facilitating communication and knowledge exchange among neural networks, leveraging the roles of both students and teachers. In our proposed framework, each node represents a neural network, capable of acting as either a student or a teacher. When new data is introduced and a network has not been trained on it, the node assumes the role of a student, initiating a communication process. The student node communicates with potential teachers, identifying those networks that have already been trained on the incoming data. Subsequently, the student node employs knowledge distillation techniques to learn from the teachers and gain insights from their accumulated knowledge. This approach enables efficient and effective knowledge transfer within the neural network ecosystem, enhancing learning capabilities and fostering collaboration among diverse networks. Experimental results demonstrate the efficacy of our framework in improving overall network performance and knowledge utilization.